Design of a Soft Sensor for Monitoring Phosphorous Uptake in an EBPR Process

Abstract

Phosphorus (P) is a key nutrient targeted for removal by wastewater treatment, increasingly being achieved using biological processes such as enhanced biological phosphate removal (EBPR). However, commercial instrumentation for automated measurement of P is costly and provides only limited temporal resolution, constraining implementation of real-time controls in EBPR processes. This study designs a soft sensor for real-time controls using a suite of relatively low-cost sensors (ion-selective electrodes) to monitor P removal by measuring (1) secondary cations tightly coupled to bioP metabolism and (2) process bulk chemistry. Data collected from a highly instrumented lab scale reactor are used to evaluate which and how many sensors are required to achieve this goal. Machine learning (ML) approaches (support vector machines, nonlinear logistic regression, random forest, and Bayesian classification) are evaluated for sensor data fusion and coupled with a decision metric to operationalize the algorithm for reactor controls. Two key results emerge: (1) use of the slope of sensor data (mV/min) rather than raw data (mV) as the predictor signals significantly improves accuracy and resilience of the soft sensor-based system and (2) the K+ ion-selective electrode, in combination with any of the four ML algorithms, is sufficient to detect completion of P removal (within study sampling granularity of 4 min) with 100% accuracy in real time. High accuracy is maintained even as process chemistry is varied to increase the interference experienced by the sensors, indicating that this soft sensor is viable for use in real wastewater applications.

Perspective: Phosphorus monitoring must be rooted in sustainability frameworks spanning material scale to human scale

“…More recently, Nair et al. (2020) and Zhang et al. (2022) developed soft sensor CPS for predicting P concentration in wastewater bioreactors based on real time surrogate sensors (see supplemental Table S1).…”

A Review on Applications of Artificial Intelligence in Wastewater Treatment

“…Most studies use 60-80% data for training and the remaining data for testing. Some researchers have made an attempt to design various sensors to enable rapid and accurate real-time monitoring and WWT process automation by real-time sensing, data analysis and online controls [57][58][59][60]. For the monitoring of influent water, some water quality parameters, such as BOD, COD, pH, DO, flow rate, temperature and initial pollutant concentration, are easily obtained and used for the inputs of AI models, while for the monitoring of effluent water, some water quality parameters, such as effluent BOD, COD, pH, DO and pollutant concentration, are usually used to evaluate the effect of WWT or the performance of wastewater treatment plants (WWTPs).…”